Interaction between Pathogenic Bacteria and Intrauterine Leukocytes Triggers Alternative Molecular Signaling Cascades Leading to Labor in Women
Bibliographic record
Abstract
Increased risk of preterm labor has been linked to cervicovaginal infection with Ureaplasma urealyticum and group B streptococci. Although various experimental models have been developed to study the role of amniochorion infection in preterm labor, they typically exclude the initial interaction between intrauterine leukocytes (recruited from decidual vessels into the avascular fetal membranes) and infecting bacteria. In this work, we ascertained whether inflammatory molecules secreted by bacterium-activated intrauterine leukocytes stimulate the amniochorion production of mediators involved in human labor. Using a two-step process beginning with placental circulating leukocytes as a proxy for intrauterine leukocytes, we found that coincubation of amniochorion explants with plasma from placental whole blood preincubated with group B streptococci resulted in a significant increase in tumor necrosis factor alpha (TNF-α) and matrix metalloproteinase 9 (MMP-9) levels in tissue. Extensive changes in the connective tissue arrangement and a decrease in collagen content demonstrated the degradation of the extracellular matrix following this treatment. In contrast, plasma from blood preconditioned with U. urealyticum induced a highly significant secretion of interleukin-1β (IL-1β) and prostaglandin E(2) (PGE(2)) by the amniochorion without changes in the extracellular matrix organization or content. These data demonstrate that group B streptococci induce degradation of the amniochorion as a result of MMP-9 production, probably via TNF-α, whereas U. urealyticum stimulates the secretion of PGE(2), probably via IL-1β, potentially stimulating myometrial contraction. Our study provides novel evidence that the immunological cells circulating within the uterine microenvironment respond differentially to an infectious agent, triggering alternative molecular signaling pathways leading to human labor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".